Instructions to use vmpsergio/df0fb091-2f67-40a0-88c9-b3cf1e03b1a0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vmpsergio/df0fb091-2f67-40a0-88c9-b3cf1e03b1a0 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("peft-internal-testing/tiny-dummy-qwen2") model = PeftModel.from_pretrained(base_model, "vmpsergio/df0fb091-2f67-40a0-88c9-b3cf1e03b1a0") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from vmpsergio/df0fb091-2f67-40a0-88c9-b3cf1e03b1a0: direct link, hf CLI and curl.
- Browser
- Download file 32.6 kB
-
https://huggingface.co/vmpsergio/df0fb091-2f67-40a0-88c9-b3cf1e03b1a0/resolve/main/adapter_model.bin
- Command line
-
hf download hf://vmpsergio/df0fb091-2f67-40a0-88c9-b3cf1e03b1a0/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/vmpsergio/df0fb091-2f67-40a0-88c9-b3cf1e03b1a0/resolve/main/adapter_model.bin
32.6 kB
- Xet hash:
- a3cb75ccf24a1f873b8202bb2a8deaf654f41d3b7112ba598cd479ee6f2d18c6
- Size of remote file:
- 32.6 kB
- SHA256:
- c04746e0aa39ee3e51dc80ab3f226b7f163518238cc7cdc49a5e805343dbe0fc
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